Multi-Scale Selective Residual Learning for Non-Homogeneous Dehazing
As the particles in hazy medium cause the absorption and scattering of light, the images captured under such environment suffer from quality degradation such as low contrast and color distortion. While numerous single image dehazing methods have been proposed to reconstruct clean images from hazy images, non-homogeneous dehazing has been rarely studied. In this paper, we design an end-to-end network to remove non-homogeneous dense haze. We employ the selective residual blocks to adaptively improve the visibility of resulting images, where the input feature and the residual feature are combined with fully trainable weights. Experimental results including the ablation study demonstrate that the proposed method is a promising tool for non-homogeneous dehazing that enhances the contrast of hazy images effectively while restoring colorful appearance faithfully.